adaptive-classifier (Adaptive Classifier)

codelion

posted an update about 19 hours ago

Post

2284

Introducing Dhara-70M: A diffusion language model that achieves 3.8x higher throughput than autoregressive models!

Key findings from our research on optimal architectures for small language models:

→ Depth beats width: 32 layers outperforms 12 layers at the same parameter count
→ Best-in-class factuality: 47.5% on TruthfulQA
→ 10x training efficiency using WSD (Warmup-Stable-Decay) conversion
→ Canon layers add only 0.13% parameters but improve reasoning

We trained on 1B tokens using the optimal 50-30-20 dataset mix (PDFs + filtered web + educational content), then converted to diffusion with just 100M additional tokens.

Blog: https://huggingface.co/blog/codelion/optimal-model-architecture
Model: codelion/dhara-70m

1 reply

·

codelion

posted an update 7 days ago

Post

2316

Introducing PTS Visualizer - an interactive tool for exploring how language models reason!

Visualize pivotal tokens, thought anchors, and reasoning circuits. See which tokens and sentences significantly impact success probability, explore embedding clusters, and trace reasoning step-by-step.

Try it: codelion/pts-visualizer

Explore PTS datasets:
- Qwen3-0.6B: codelion/Qwen3-0.6B-pts
- DeepSeek-R1: codelion/DeepSeek-R1-Distill-Qwen-1.5B-pts

Or upload your own JSONL files!

GitHub: https://github.com/codelion/pts

codelion

posted an update 19 days ago

Post

2561

Recently, Essential AI released a new 8B base model EssentialAI/rnj-1 they highlighted the importance of data mix for pretraning -

"In the long run, we expect our methods to automatically represent, transform, and blend data to optimize measurable abilities in pre-training. Our work on modeling data taxonomies led to new approaches for jointly clustering and mixing data distributions under data repetition penalties. Many improvements in our STEM abilities can be traced back to this. "

This resonates with the recent work we did around optimal dataset mixing for pretraining where we saw have the right mix can increase the efficiency of training -
https://huggingface.co/blog/codelion/optimal-dataset-mixing

codelion

posted an update 21 days ago

Post

2655

NotebookLM's infographics feature is amazing, it generates poster-type images from any text. Here is one I tried for my new HF article on ellora - https://huggingface.co/blog/codelion/ellora-lora-recipes

codelion

posted an update 23 days ago

Post

2296

Perplexity released a dataset (BrowseSafe) and benchmark to catch and prevent malicious prompt-injection instructions in real-time.

We trained a prompt injection classifier on BrowseSafe using adaptive-classifier with ModernBERT-base embeddings.

74.9% F1 on detecting prompt injection in web content.

Model -> adaptive-classifier/browsesafe
Dataset -> perplexity-ai/browsesafe-bench
Repo -> https://github.com/codelion/adaptive-classifier

1 reply

·

codelion

updated a model 23 days ago

adaptive-classifier/browsesafe

Text Classification • Updated 23 days ago • 49 • 1

codelion

published a model 23 days ago

adaptive-classifier/browsesafe

Text Classification • Updated 23 days ago • 49 • 1

codelion

posted an update 24 days ago

Post

1600

I just published Ellora - 6 production-ready LoRA recipes for enhancing LLMs with specific capabilities. Each recipe costs under $100 to run and includes complete training code, data generation, and evaluation.

The 6 Recipes:
Recipe 1: Accuracy Recovery - Recover 75% of quantization losses with self-distillation
Recipe 2: Reasoning LoRA - Add structured thinking with GRPO (0% to 60% adoption, 75% quality boost)
Recipe 3: Tool Calling - Real execution on actual codebases
Recipe 4: Context Extension - Scale from 32K to 2M tokens (61x increase)
Recipe 5: Secure Code Generation - 97% vulnerability reduction using automated Semgrep analysis
Recipe 6: Execution-Aware World Models - Teaching models runtime behavior

Why Recipes?
Ellora provides methodologies, not frameworks. Use them with your existing tools (PEFT, LoRAX, vLLM, Unsloth, HuggingFace). Each recipe uses self-supervised data generation (Magpie approach) - no expensive human labeling required.

All recipes include Jupyter notebooks you can run immediately with clear success metrics.

GitHub: https://github.com/codelion/ellora
Full Article: https://huggingface.co/blog/codelion/ellora-lora-recipes

Built something with these recipes? I'd love to see what you create!

codelion

in adaptive-classifier/chayan 25 days ago

Add `pipeline_tag: text-classification` and improve link visibility

#1 opened 25 days ago by

nielsr

codelion

posted an update about 1 month ago

Post

1985

Introducing OpenEvolve Prompt Optimizer - a Space that automatically evolves and optimizes your prompts using OpenEvolve!

This tool uses OpenEvolve to iteratively improve prompts by testing them on real datasets and evolving better versions. No more manual prompt engineering guesswork - let OpenEvolve find the optimal prompts for you.

How it works:
- Enter your initial prompt using {input} as a placeholder for dataset inputs
- Input any HuggingFace dataset name you want to use for optimization
- Specify the dataset split and field names for your use case
- Click Optimize Prompt and the system will validate everything first
- Compare your initial prompt vs the evolved best prompt side-by-side

Try it here: algorithmicsuperintelligence/prompt-optimizer

OpenEvolve GitHub: https://github.com/algorithmicsuperintelligence/openevolve

codelion

updated a model about 1 month ago

adaptive-classifier/chayan

Text Classification • Updated 25 days ago • 26 • 3

codelion

posted an update about 1 month ago

Post

2469

🎯 Introducing Chayan: A Calibrated 4-Model LLM Router Achieving 69% Accuracy on RouterArena

We're excited to share Chayan, a cost-efficient LLM router that intelligently routes queries between 4 models to maximize accuracy while minimizing cost. Chayan just submitted to the RouterArena leaderboard and achieved 69.05% accuracy on the benchmark!

🔗 Model: adaptive-classifier/chayan
🔗 Dataset: RouteWorks/RouterArena

📊 Performance Highlights

Chayan achieves impressive results on the RouterArena benchmark:
• 69.05% accuracy (would rank #1 on current leaderboard)
• $0.333 per 1K queries
• +12.07pp improvement over all-mini baseline (56.98%)
• 99% of perfect 2-model oracle performance at 57% lower cost

Compared to our previous 2-model router (61.43% accuracy), Chayan delivers +7.62pp improvement through smarter 4-model routing.

🧠 How It Works

Chayan uses an Adaptive K-NN classifier with prototype memory to route between 4 models:
• openai/gpt-4o-mini (fast & cheap)
• google/gemini-2.5-flash-lite (balanced)
• google/gemini-2.5-flash (capable)
• openai/gpt-4o (most powerful)

🚀 Getting Started

You can use Chayan directly from HuggingFace:

from adaptive_classifier import AdaptiveClassifier

Load Chayan
router = AdaptiveClassifier.load("adaptive-classifier/chayan")

Route a query
query = "What is the capital of France?"
predictions = router.predict(query, k=4)

Get top model recommendation
best_model = predictions[0][0]
print(f"Recommended model: {best_model}")

Built with the adaptive-classifier library: https://github.com/codelion/adaptive-classifier

codelion

published a model about 1 month ago

adaptive-classifier/chayan

Text Classification • Updated 25 days ago • 26 • 3

codelion

posted an update about 2 months ago

Post

3987

Want to experiment with pre-training dataset mixtures but don't want to process terabytes of data? We've got you covered.

We're releasing a collection of several carefully curated 1B token dataset samples specifically designed for rapid prototyping and pretraining experiments: https://huggingface.co/collections/codelion/pre-training-dataset-samples

These samples were created using reservoir sampling - an algorithm that guarantees statistically unbiased random samples from massive source datasets. This means results you get at the 1B token scale are representative of how these datasets behave at 100B+ token scales, letting you iterate quickly without the computational overhead.

The collection includes:
- finePDFs-1B: High-quality textbook-style educational content
- DCLM-baseline-1B: Filtered, diverse web content
- FineWeb-Edu-1B: Curated educational web resources

We used these exact samples to run 50+ systematic experiments on dataset mixing strategies, ultimately discovering that a 50-30-20 mixture of finePDFs + DCLM-baseline + FineWeb-Edu achieves 90%+ of GPT-2's performance with just 1/10th the training data.

Whether you're researching optimal data mixtures, testing curriculum learning strategies, or just want to quickly prototype a pretraining run, these samples give you a solid foundation to start experimenting immediately.

Read the full story of how we used these datasets to find the optimal pretraining recipe: https://huggingface.co/blog/codelion/optimal-dataset-mixing

1 reply

·

codelion

posted an update about 2 months ago

Post

276

MARS Achieves Strong Results on Google DeepMind's IMO-Bench

We evaluated OptiLLM's MARS (Multi-Agent Reasoning System) approach on IMO-Bench, Google DeepMind's challenging mathematical reasoning benchmark with International Mathematical Olympiad-level problems.

What is MARS?

MARS is a multi-agent reasoning technique that works with any LLM. It uses 3 parallel reasoning agents that independently solve problems, then verifies their solutions through consensus and iterative refinement. The key advantage: it's model-agnostic and can be applied to any base model through OptiLLM's inference proxy.

Results on IMO-Bench:

AnswerBench (400 short-answer problems):
MARS: 36.0% (144/400 correct)
Baseline: 24.5% (98/400 correct)
Improvement: +11.5pp across all domains

Category breakdown:
- Algebra: 33% (vs 21% baseline)
- Combinatorics: 26% (vs 19% baseline)
- Geometry: 43% (vs 28% baseline)
- Number Theory: 42% (vs 30% baseline)

ProofBench (60 proof construction problems):
MARS: 26.7% (16/60 correct)
Baseline: 18.3% (11/60 correct)
Improvement: +8.4pp

Category breakdown:
- Number Theory: 42.9% (vs 14.3% baseline)
- Combinatorics: 37.5% (vs 31.2% baseline)
- Algebra: 18.8% (vs 25.0% baseline)
- Geometry: 7.1% (vs 0.0% baseline)

All results achieved using google/gemini-2.5-flash-lite-preview-09-2025 as the base model. The same MARS approach can enhance reasoning for any model through OptiLLM's OpenAI-compatible API.

Datasets available at:
AnswerBench: huggingface.co/datasets/Hwilner/imo-answerbench
ProofBench: huggingface.co/datasets/Hwilner/imo-proofbench

Try it yourself:

python optillm.py --approach mars --model google/gemini-2.5-flash-lite-preview-09-2025

Or via API with approach prefix:

model: "mars-google/gemini-2.5-flash-lite-preview-09-2025"

Full evaluation code and results available at: github.com/algorithmicsuperintelligence/optillm

codelion

posted an update about 2 months ago

Post

3606

On this day in 2019, OpenAI released the final GPT-2 model as part of their staged release. I still remember that November well - so much was happening, but GPT-2's release felt like a watershed moment for the field. It showed us what was possible with carefully trained language models.

To recreate some of that GPT-2 magic, I recently tackled an interesting challenge: can you pretrain a language model with just 1 billion tokens - roughly 1/10th of what GPT-2 used - and still get comparable performance? After 50+ systematic experiments testing different dataset mixtures, the answer is yes.

The result is codelion/gpt-2-70m, which achieves over 90% of GPT-2's benchmark performance despite being trained on 10x less data. The key was finding the optimal dataset composition: 50% high-quality textbook PDFs, 30% filtered web content, and 20% educational resources. It even beats GPT-2 on TruthfulQA (47.31% vs 40.69%).

If you're interested in the full story of how we discovered this optimal mixture and why curriculum learning catastrophically failed, check out the complete article: https://huggingface.co/blog/codelion/optimal-dataset-mixing

Sometimes less really is more - when you mix it right.

1 reply

·

codelion

posted an update about 2 months ago

Post

391

The 1 Billion Token Challenge: Finding the Perfect Pre-training Mix

We trained a GPT-2 model to 90%+ performance using just 1/10th the training data through 50+ systematic experiments on dataset mixing strategies.

Key Finding:

A static mix of 50% finePDFs + 30% DCLM-baseline + 20% FineWeb-Edu consistently outperforms complex curriculum learning approaches. Static mixing is simpler, faster, and avoids catastrophic failures from hard distribution shifts.

Results:

Our GPT-2-70M model (70M parameters, 1B tokens) scores 38.15% on benchmarks vs GPT-2's 39.13% - only 0.98 points behind despite 10x less data and 44% fewer parameters. It even beats GPT-2 on TruthfulQA (47.31% vs 40.69%).

The takeaway: careful dataset curation matters more than total data volume.

Model: codelion/gpt-2-70m

Datasets: https://huggingface.co/collections/codelion/pre-training-dataset-samples

Full blog: https://huggingface.co/blog/codelion/optimal-dataset-mixing

codelion

posted an update 2 months ago

Post

3258

🧠 Introducing Ellora Recipe #6: Execution-Aware World Model for Qwen3-4B-Thinking

Teaching LLMs to understand not just what code does, but HOW it executes at runtime!

Inspired by Meta's CWM (Code World Model) research, this LoRA adapter adds execution awareness to Qwen3-4B-Thinking-2507. The model learns to predict variable states, trace program execution step-by-step, and debug code by understanding runtime behavior.

🔍 Key Innovation:
We combine Qwen3's native thinking capabilities with real Python execution traces captured via sys.settrace(). The model is trained using GRPO with a custom reward function that scores execution prediction accuracy.

📊 Training Approach:
- Hybrid Magpie-style code generation
- Real execution tracing for ground truth
- Self-supervised learning (no manual annotations!)
- 298 training samples with execution traces

✨ What it does:
- Predicts variable states at each line of code
- Explains execution flow with thinking tags
- Helps debug by understanding runtime behavior
- Works as a "neural debugger"

🎯 Results:
- 20% overall accuracy on execution prediction
- 33.3% mean state accuracy
- Trained on Qwen3-4B-Thinking (262K context, 4B params)

🔗 Links:
Model: codelion/Qwen3-4B-execution-world-model-lora
Dataset: codelion/execution-world-model-dataset
GitHub Recipe: https://github.com/codelion/ellora
Notebook: https://github.com/codelion/ellora/blob/main/Ellora_Recipe_6_Execution_World_Model_Thinking_LoRA.ipynb

Part of the Ellora project - standardized LoRA recipes for enhancing LLM capabilities. All recipes use self-supervised data generation and work with existing infrastructure (PEFT, LoRAX, vLLM).

#LLM #LoRA #CodeGeneration #WorldModel #Qwen #AI #MachineLearning

codelion

posted an update 3 months ago

Post

1891

🚀 Adaptive Classifier v0.1.0: Now with ONNX Runtime Support!

We're excited to announce a major update to Adaptive Classifier - a flexible, continuous learning classification system that adapts to new classes without retraining!

What's New:

⚡ ONNX Runtime Integration: Get 1.14x faster CPU inference out of the box (up to 4x on x86 processors)

📦 INT8 Quantization: Models are now 4x smaller with minimal accuracy loss, making deployment easier and faster

🎯 Smart Loading: Automatically uses the best model variant for your hardware - quantized for speed by default, or unquantized for maximum accuracy

🔄 7.5x Faster Model Loading: Get started quickly with optimized model initialization

How It Works:

Adaptive Classifier lets you build text classifiers that continuously learn from new examples without catastrophic forgetting. Perfect for:
- Dynamic classification tasks where classes evolve over time
- Few-shot learning scenarios with limited training data
- Production systems that need to adapt to new categories

The new ONNX support means you get production-ready speed on CPU without any code changes - just load and run!

Try it now:

from adaptive_classifier import AdaptiveClassifier

# Load with ONNX automatically enabled (quantized for best performance)
classifier = AdaptiveClassifier.load("adaptive-classifier/llm-router")

# Add examples dynamically
classifier.add_examples(
["Route this to GPT-4", "Simple task for GPT-3.5"],
["strong", "weak"]
)

# Predict with optimized inference
predictions = classifier.predict("Complex reasoning task")

Check out our LLM Router model to see it in action:
adaptive-classifier/llm-router

GitHub Repository:
https://github.com/codelion/adaptive-classifier

Install now: pip install adaptive-classifier

We'd love to hear your feedback and see what you build with it!

#MachineLearning #NLP #ONNX #ContinuousLearning #TextClassification

codelion

updated a model 3 months ago

adaptive-classifier/llm-router

Text Classification • Updated Oct 6 • 98 • 7

Adaptive Classifier

AI & ML interests

Recent Activity

adaptive-classifier/browsesafe

adaptive-classifier/browsesafe

Add `pipeline_tag: text-classification` and improve link visibility

adaptive-classifier/chayan

adaptive-classifier/chayan

adaptive-classifier/llm-router

AI & ML interests

Recent Activity

Team members 3

adaptive-classifier's activity

Add `pipeline_tag: text-classification` and improve link visibility